Scholarlink

<p>Wikipedia is an essential source of information online, making the integrity of its content critical to the health of the information ecosystem. Yet few studies have examined how Wikipedia’s volunteer-driven editorial governance responds to pressure during politically sensitive periods. We address this gap by investigating editing dynamics on US politicians’ Wikipedia pages during the 2024 presidential election cycle. Using a combination of quantitative methods—including revert risk prediction and change point detection—and qualitative manual annotation of over 1700 edits, we analyse how editorial activity, revert patterns, and governance mechanisms respond to major political events. Our results show that both edit volume and revert risk escalate significantly during key political moments, with Republican-affiliated pages—particularly Donald Trump’s—receiving substantially more editing activity and higher revert risk than Democratic counterparts. Wikipedia’s reversion mechanisms respond swiftly to high-risk edits, but lower-risk problematic content can persist for extended periods. Manual annotation revealed that while overt misinformation was rare in our sample (approximately 1% of coded edits), the cases identified were disproportionately characterised by biased language rather than factual inaccuracies, suggesting that subtler forms of manipulation pose the greater challenge.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Editorial governance under pressure: Wikipedia's revert dynamics during the 2024 US election

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月13日

查看全文
  • Giuliano Formisano,
  • Emmie Hine,
  • Prathm Juneja,
  • Joel Laitila,
  • Claudio Novelli,
  • Ethan Chiu,
  • Elizabeth Dejanikus,
  • Madeline Levin,
  • Tyler Schroder,
  • Andrew West,
  • Luciano Floridi

摘要

Wikipedia is an essential source of information online, making the integrity of its content critical to the health of the information ecosystem. Yet few studies have examined how Wikipedia’s volunteer-driven editorial governance responds to pressure during politically sensitive periods. We address this gap by investigating editing dynamics on US politicians’ Wikipedia pages during the 2024 presidential election cycle. Using a combination of quantitative methods—including revert risk prediction and change point detection—and qualitative manual annotation of over 1700 edits, we analyse how editorial activity, revert patterns, and governance mechanisms respond to major political events. Our results show that both edit volume and revert risk escalate significantly during key political moments, with Republican-affiliated pages—particularly Donald Trump’s—receiving substantially more editing activity and higher revert risk than Democratic counterparts. Wikipedia’s reversion mechanisms respond swiftly to high-risk edits, but lower-risk problematic content can persist for extended periods. Manual annotation revealed that while overt misinformation was rare in our sample (approximately 1% of coded edits), the cases identified were disproportionately characterised by biased language rather than factual inaccuracies, suggesting that subtler forms of manipulation pose the greater challenge.